Background Field
Embodiments of the subject matter described herein are related generally to using a computer vision based pose and an inertial sensor based pose separately in a visualization application, and more specifically to displaying separate graphics for the computer vision based pose and the inertial sensor based pose to prompt user movement of the mobile device.
Relevant Background
An increasing number of mobile devices, such as smartphones, tablet computers, cameras, etc., include various types of inertial sensors in addition to a camera. Inertial sensors, such as accelerometers and gyroscopes, are useful for tracking movement of a mobile device due to their ability to measure acceleration and angular velocity at high sampling rates. Many applications, such as augmented reality, indoor navigation, etc., use a combination of inertial sensors and computer vision for a determination of the pose (position and orientation) and tracking of the mobile device.
In order for inertial sensors to be used for accurate position determination, particularly when combined with computer vision techniques, accurate calibration of the inertial sensors is required. Calibration of the inertial sensors includes alignment of inertial sensors with respect to camera, as well as calibration of scale and non-orthogonality parameters of the inertial sensors. Calibration of inertial sensors, however, is currently a time consuming factory process, which is required for each individual mobile device. During factory calibration of inertial sensors, a series of precise movements of the mobile device is performed and correlated to the response of the inertial sensors. While some approaches for calibration of sensors with user motion exist, the performance of such approaches is heavily dependent on supplying user motions that are similar in quality to factory motions. Instructing users to provide complicated and yet highly accurate motion is difficult. Without these rich motions, however, factory calibration, with its accompanying increase in cost, is the only way to get reliably obtain accurate calibration parameters.